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Top 5 Books to Learn Data Science in 2022 For Complete Beginners

Top 5 Books to Learn Data Science in 2022 For Complete Beginners

So you've learned the data science prerequisites and you're ready for the real deal? That's where this list of top 5 curated data science books comes in. We'll share our top picks any beginner can follow when first learning data science.

These books have helped thousands of people learn data analysis, visualization, advanced programming skills, machine learning, and much more - even to land a job! Let's dive straight in.

Disclaimer:  The article contains affiliate links to our top recommended books. That doesn’t mean anything to you, as the price is identical, but we’ll get a small commission if you decide to make a purchase.

Python for Data Analysis - Learn Programming Skills for Data Science

Image 1 - Python for Data Analysis -

A great read and obvious next step if you have the basic Python programming skills. The book covers pretty much every possible method of data analysis, alongside the basics of the Python programming language.

One thing we particularly like about this book is that the author gives you a good idea of what you should expect from working as a data analyst/scientist. To conclude, the book is very well organized and a pleasure to read, it’s perfectly paced and everything is explained simply.

Expect to get a deep dive into Pandas, storage formats, methods of data preprocessing, wrangling, grouping, and even some basic data visualization and time series analysis - all in around 500 pages.

You can learn more about the book on Amazon.

Fundamentals of Data Visualization - A Picture is Worth a Thousand Words

Image 2 - Fundamentals of Data Visualization -

What's the best way to effectively communicate the results of your analysis? That's right - data visualization. This book walks you through the most common data visualization problems and teaches you what's the best visualization type for which occasion.

The book is only around 350 pages long, but covers all required topics - color scales, bar charts, distributions, QQ-plots, pie charts, mosaic plots, treemaps, scatter plots, time series, geospatial data, and much more. It also teaches you the principles of effective chart design, which is a must-know skill for data scientists.

You can learn more about the book on Amazon.

Storytelling With Data - Learn to Convey the Right Message

Image 3 - Storytelling With Data -

This book was written around a simple premise - showing your data isn't enough, you should tell a story with it. It's an excellent follow-up read from the previous book on this list, as you're already familiar with the basic data visualization principles.

You'll learn why context is an extremely important thing to think about before choosing an effective data visualization. You'll also learn how to think as a designer, and how to avoid clutter in your data visualizations.

The book is only around 250 pages long, so expect to go through it in no time.

You can learn more about the book on Amazon.

Data Science From Scratch - Excellent Refresher and Much More

Image 4 - Data Science from Scratch -

Learning data science tools and libraries only isn't enough. You should understand the underlying principles, and that's where this book comes on. Of course, you already know the fundamentals if you've read the books from our prerequisites list. This book provides a nice recap and much more.

This 400-page book will give you a refresher in Python programming, data visualization, linear algebra, statistics, hypothesis testing, and will teach you basic principles of working with data and machine learning algorithms - from simple linear regression to deep learning, NLP, and recommender systems.

You can learn more about the book on Amazon.

Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow - A Must Read

Image 5 - Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow - 

This book is massive - 800+ pages! But you're in for a treat. It's a long-time best-seller on Amazon, because it covers everything one might need to work in the field, explained with perfect clarity. Seriously, the book covers topics from the machine learning definition to GANs and reinforcement learning.

The book goes from simple topics, like data gathering, EDA, feature scaling, to actual machine learning through algorithms such as decision trees, random forest, and gradient boosting. It also covers the main dimensionality reduction techniques and unsupervised learning. All of that in the first 300 pages!

The rest is reserved for neural networks and deep learning, from theory to application in the TensorFlow library. Expect to learn a lot about ANNs, CNNs, RNNs, Autoencoders, GANs, and reinforcement learning.

Another no-brainer if you have the time. And will.

You can learn more about the book on Amazon.


To conclude, data science is a broad field, and you're expected to bring numerous skills to the table. These five books are a great place to start. After reading, you'll be ready to apply the newly-acquired knowledge to the area that interests you.

Expect to spend 6-12 months going through the materials if you want a deep understanding. How long it will actually take depends on your prior knowledge and the amount of time you have available.

There's no better time to start than today. You've got this!

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